Dimensional Model Research Articles

Dimension reduction based on time-limited cross Gramians for bilinear systems

The cross Gramian is a useful tool in model order reduction but only applicable to square dynamical systems. Throughout this paper, time-limited cross Gramians is firstly extended to square bilinear systems that satisfies a generalized Sylvester equation, and then concepts from decentralized control are used to approximate a cross Gramian for non-square bilinear systems. In order to illustrate these cross Gramians, they are calculated efficiently based on shifted Legendre polynomials and applied to dimension reduction, which leads to a lower dimensional model by truncating the states that are associated with smaller approximate generalized Hankel singular values. In combination of the dominant subspace projection method, our reduction procedure is modified to produce a bounded-input bounded-output stable-preserved reduced model under some certain conditions. At last, the performance of numerical experiments indicates the validity of our reduction methods.

Testing sufficiency for transfer learning

Modern statistical analysis often encounters high dimensional models but with limited sample sizes. This makes it difficult to estimate high-dimensional statistical models based on target data with limited sample size. Then how to borrow information from another large sized source data for more accurate target model estimation becomes an interesting problem. This leads to the useful idea of transfer learning. Various estimation methods in this regard have been developed recently. In this work, we study transfer learning from a different perspective. Specifically, we consider here the problem of testing for transfer learning sufficiency. We denote transfer learning sufficiency to be the null hypothesis. It refers to the situation that, with the help of the source data, the useful information contained in the feature vectors of the target data can be sufficiently extracted for predicting the interested target response. Therefore, the rejection of the null hypothesis implies that information useful for prediction remains in the feature vectors of the target data and thus calls for further exploration. To this end, we develop a novel testing procedure and a centralized and standardized test statistic, whose asymptotic null distribution is analytically derived. Simulation studies are presented to demonstrate the finite sample performance of the proposed method. A deep learning related real data example is presented for illustration purpose.

The Impact of Adverse Childhood Experiences on Cognitive Control Across the Lifespan: A Systematic Review and Meta-analysis of Prospective Studies.

Adverse childhood experiences (ACEs) are strongly associated with impaired cognitive control, yet research on ACEs' effects across cognitive control domains-working memory, cognitive flexibility, and inhibitory control-remains sparse. This systematic review and meta-analysis evaluated the overall impact of ACEs on each of these cognitive control domains and explored moderating factors such as age, gender, cognitive control paradigms, and ACEs subtypes based on the dimensional model of adversity and psychopathology. A database search was conducted in SCOPUS, MedLine, PsycINFO, and Web of Science. Only prospective studies were included to ensure temporal order inferences, with at least two data collection points, assessing ACEs at baseline (T1) and cognitive control during follow-up (T2). Thirty-two studies (N = 26,863) producing 124 effect sizes were analyzed. Three-level meta-analyses revealed small-to-medium negative associations between ACEs and overall cognitive control (g = -0.32), and in each domain: working memory (g = -0.28), cognitive flexibility (g = -0.28), and inhibitory control (g = -0.32). The negative associations between ACEs and cognitive control were consistent across age, gender, and cognitive control paradigms. ACEs subtypes moderated the association with cognitive flexibility (p = .04) but not working memory or inhibitory control. Specifically, the deprivation subtype exhibited a stronger negative association with cognitive flexibility compared to threat and threat-and-deprivation subtypes. These findings highlight the pervasive negative impact of ACEs on cognitive control across ages and emphasize the need for targeted interventions. Implications, current gaps, limitations in research, and future study recommendations are discussed.

Data-driven predictor of control-affine nonlinear dynamics: Finite discrete-time bilinear approximation of koopman operator

This paper introduces a novel and efficient data-driven approach for approximating a finite discrete-time bilinear model of control affine nonlinear dynamical systems with a tunable parameter that balances model dimension and prediction accuracy. An approximation of the Koopman operator based on the evolutions of the nonlinear system measurements used to lift a control-affine nonlinear system to a higher dimensional model. However, higher dimensional spaces can result in a long learning time and the curse of dimensionality in control analysis. The proposed approach addresses these challenges by introducing a convex optimization which identifies informative observable functions. This technique allows for the adjustment of a parameter to strike a balance between model dimension and accuracy in prediction. The main contribution of this study is to introduce a reduced dimensional bilinear model for a nonlinear complex system. This achievement is made possible by implementing convex sparse optimization, enabling the exploration of informative estimated Koopman eigenfunctions while minimizing the number of system measurements required. The optimization problem is solved using the alternating direction method of multipliers. The effectiveness of the proposed method is evaluated on three different nonlinear systems: a numerical nonlinear system, a Van der Pol oscillator, and a Duffing oscillator. In the last simulation, an estimation of the Koopman linear model is considered as a special case, and the policy iteration algorithm is employed to evaluate optimal control designed for different reduced-dimensional models.

Dimensional models of personality and a multidimensional framework for treating personality pathology

SUMMARY The categorisation of personality pathology into discrete disorders has been an enduring standard. However, dimensional models of personality are becoming increasingly prominent, in part owing to their superior validity and clinical utility. We contend that dimensional models also offer a unique advantage in treating mental illness. Namely, psychotherapy approaches and the components of dimensional models of personality can both be arranged hierarchically, from general to specific factors, and aligning these hierarchies provides a sensible framework for planning and implementing treatment. This article begins with a brief review of dimensional models of personality and their supporting literature. We then outline a multidimensional framework for treatment and present an illustrative fictitious clinical case before ending with recommendations for future directions in the field.

An advanced high dimensional model representation approach for internal combustion engine modeling and optimization

This work introduces an efficient data-driven approach for engine performance modeling and optimization. A highly accurate and robust high-dimensional model representation (HDMR) surrogate model is developed based on the dataset generated from a coupled KIVA4-CHEMKIN Ⅱ software. The HDMR model is integrated with a multi-objective optimization algorithm, enabling the automatic identification of optimal combustion strategies. Results suggest that the constructed HDMR model is able to accurately predict the engine performance and exhaust gas emissions with negligible CPU costs. Moreover, the model effectively identifies optimal engine operating conditions, leading to enhanced fuel efficiency.

Multimodal emotion recognition: A comprehensive review, trends, and challenges

AbstractAutomatic emotion recognition is a burgeoning field of research and has its roots in psychology and cognitive science. This article comprehensively reviews multimodal emotion recognition, covering various aspects such as emotion theories, discrete and dimensional models, emotional response systems, datasets, and current trends. This article reviewed 179 multimodal emotion recognition literature papers from 2017 to 2023 to reflect on the current trends in multimodal affective computing. This article covers various modalities used in emotion recognition based on the emotional response system under four categories: subjective experience comprising text and self‐report; peripheral physiology comprising electrodermal, cardiovascular, facial muscle, and respiration activity; central physiology comprising EEG, neuroimaging, and EOG; behavior comprising facial, vocal, whole‐body behavior, and observer ratings. This review summarizes the measures and behavior of each modality under various emotional states. This article provides an extensive list of multimodal datasets and their unique characteristics. The recent advances in multimodal emotion recognition are grouped based on the research focus areas such as emotion elicitation strategy, data collection and handling, the impact of culture and modality on multimodal emotion recognition systems, feature extraction, feature selection, alignment of signals across the modalities, and fusion strategies. The recent multimodal fusion strategies are detailed in this article, as extracting shared representations of different modalities, removing redundant features from different modalities, and learning critical features from each modality are crucial for multimodal emotion recognition. This article summarizes the strengths and weaknesses of multimodal emotion recognition based on the review outcome, along with challenges and future work in multimodal emotion recognition. This article aims to serve as a lucid introduction, covering all aspects of multimodal emotion recognition for novices.This article is categorized under: Fundamental Concepts of Data and Knowledge > Human Centricity and User Interaction Technologies > Cognitive Computing Technologies > Artificial Intelligence

Isospinning CP2 solitons

We study stationary rotating topological solitons in a (2+1)-dimensional CP2 nonlinear sigma model with a stabilizing potential term. We find families of U(1)×U(1) symmetric solutions with topological degrees equal or larger than 1, which have two angular frequencies and are labeled by two (one topological and the other nontopological) winding numbers k1≥k2. We discuss properties of these solitons and investigate the domains of their existence. Published by the American Physical Society 2024

Lie-bäcklund symmetry, soliton solutions, chaotic structure and its characteristics of the extended (3 + 1) dimensional Kairat-II model

AbstractThis study investigates the (3 + 1)-dimensional extended Kairat-II model using Lie-Bäcklund symmetry (LBS) and the improved modified extended tanh-function approach (IMETFA). The bifurcation and sensitivity analyses are conducted to understand the stability and chaotic behavior of the model. Through graphical visualizations of phase diagrams, Lyapunov exponents, power spectra, fractal dimension and recurrence plots, the complex dynamics and stability characteristics of the model are elucidated. Additionally, the research focuses on the derivation of dark soliton and various combo soliton solutions of the Kairat-II model. The derived soliton solutions are graphically displayed in 3D and 2D plots. The obtained results are new and have never been reported in the literature for the considered equation.

Leveraging Dimensional Modeling for Optimized Healthcare Data Warehouse Cloud Migration: Data Masking and Tokenization

Leveraging Dimensional Modeling for Optimized Healthcare Data Warehouse Cloud Migration: Data Masking and Tokenization

High-order solitary waves, fission, hybrid waves and interaction solutions in the nonlinear dissipative (2+1)-dimensional Zabolotskaya-Khokhlov model

The Zabolotskaya-Khokhlov model (ZKm) is a widely used nonlinear model in the fields of sound, ultrasound, and shock waves. The aims of this paper stems from its examination and rectification of earlier results concerning the N-soliton solutions of nonlinear dissipative (2+1)-dimensional ZKm. By recognizing and incorporating the non-zero values of the dispersion coefficient , this study addresses a significant omission in current research. The findings enhance the comprehension of higher-order soliton behaviors, encompassing bifurcation solitons, higher-order breathers, rogue waves, periodic lumps, and their interactions, which are crucial for both theoretical studies and practical applications in areas like nonlinear optics and fluid dynamics. Subsequent detailed numerical simulations are conducted to elucidate the complex behaviors of the obtained solutions. This thorough exploration provides crucial insights into the intricate patterns exhibited by the nonlinear dissipative (2+1)-dimensional ZKm under different conditions, enhancing our understanding of the underlying physical phenomena.

Towards field theory of multiple D0-branes. Hamiltonian mechanics and quantization of simplest 3D prototype of multiple D0-brane system

Recently we have constructed a completely supersymmetric nonlinear action possessing the properties expected from multiple D0-brane system. Its quantization should result in an interesting supersymmetric field theory in the (super)space with additional matrix coordinates which can provide an important insights in the study of String Theory. As a first stage toward this aim, in this paper we construct the Hamiltonian mechanics and perform covariant quantization of the simplest three dimensional counterpart of the ten dimensional multiple D0-brane model. We obtain a supersymmetric system of equations in a (super)spacetime enlarged by bosonic and fermionic matrix coordinates which appears as a result of such quantization and discuss some of its properties.

Adaptation of virtual surgical planning to midface reconstruction with intraoperative navigation: “navigation mediated midface reconstruction”

ObjectiveThe production of three dimensional models and materials according to preoperative virtual surgical planning is a time consuming process and causes high costs. We aimed to demonstrate the navigation mediated reconstruction of the patients who underwent the removal of tumoral mass in midfacial region according to their preoperatively prepared surgical plannings. Study DesignPatients who underwent the removal of tumoral mass and reconstruction in their midfacial region were included in the study. Virtual surgical planning was performed by the mirror imaging of unaffected side of maxillofacial bones. New created models were converted to DICOM (Digital Imaging and Communications in Medicine) data to use in the navigation system. Histogram analysis was performed to reveal the compatibility of navigation mediated reconstruction with preoperative virtual surgical planning. ResultsReconstruction of orbital floor was achieved in all of the cases. Histogram comparison of the localizations of orbital floor and maxillary walls was calculated at a confidence level of 95 % (p > 0.95) and mean difference was found 0.85 mm; while standard deviation was 3.55 mm. ConclusionWe found that virtually prepared reconstruction of midfacial area can be succesfully adapted to the surgery with the assistance of intraoperative navigation system.

Multiple Lax integrable higher dimensional AKNS(-1) equations and sine-Gordon equations.

Through the modified deformation algorithm related to conservation laws, the (1+1)-dimensional AKNS(-1) equations are extended to a (4+1)-dimensional AKNS(-1) system. When one, two, or three of the independent variables are removed, the (4+1)-dimensional AKNS(-1) system degenerates to some novel (3+1)-dimensional, (2+1)-dimensional, and (1+1)-dimensional AKNS(-1) systems, respectively. Under a simple dependent transformation, the (1+1)-dimensional AKNS(-1) equations turn into the classical sine-Gordon equation. Then using the same deformation procedure, the (1+1)-dimensional sine-Gordon equation is generalized to a (3+1)-dimensional version. By introducing the deformation operators to the Lax pairs of the original (1+1)-dimensional models, the Lax integrability of both the (4+1)-dimensional AKNS(-1) system and the (3+1)-dimensional sine-Gordon equation is proven. Finally, the traveling wave solutions of the (4+1)-dimensional AKNS(-1) system and the (3+1)-dimensional sine-Gordon equation are implicitly given and expressed by tanh function and incomplete elliptic integral, respectively. These results may enhance our understanding of the complex physical phenomena described by the nonlinear system discussed in this paper.

The Role of Diagnostic Models in Prejudice Toward People With Borderline Personality Disorder: An Experimental Investigation.

We tested whether dimensional personality disorder models such as the Alternative Model for Personality Disorders (AMPD) cause less prejudice toward people with borderline personality disorder (BPD) than categorical models, and we sought to identify the mechanisms underpinning this reduction in prejudice. Undergraduate psychology students (n = 183) were randomly assigned to one of three conditions (AMPD, categorical, control) and given descriptive information about BPD. Participants in the AMPD and categorical conditions also received a presentation about their respective BPD diagnostic criteria. Students in all conditions then completed a survey assessing their prejudice toward people with BPD. There was no difference between conditions on overall prejudice toward people with BPD. However, the AMPD increased continuum beliefs and decreased categorical beliefs, and these, in turn, affected perceptions of people with BPD as ingroup members, which indirectly reduced prejudice. We have identified pathways through which the AMPD indirectly reduces prejudice toward people with BPD.

Deep Learning to Predict the Future Growth of Geographic Atrophy from Fundus Autofluorescence

PurposeThe region of growth (ROG) of geographic atrophy (GA) throughout the macular area has an impact on visual outcomes. Here, we developed multiple deep learning (DL) models to predict the 1-year ROG of GA lesions using fundus autofluorescence (FAF) images. DesignIn this retrospective analysis, 3 types of models were developed using FAF images collected 6 months after baseline to predict the GA lesion area (segmented lesion mask) at 1.5 years, FAF images collected at baseline and 6 months to predict the GA lesion at 1.5 years, and FAF images collected 6 months after baseline to predict the GA lesion at 1 and 1.5 years. The 1-year ROG from the 6-month visit was derived by taking the difference between the GA lesion area (segmented lesion mask) at the 1.5-year and 6-month visits. ParticipantsPatients enrolled in the following lampalizumab clinical trials and prospective observational studies: NCT02247479, NCT02247531, NCT02479386, and NCT02399072. MethodsDatasets of study eyes from 597 patients were split into model training (310), validation (78), and test sets (209), stratified by baseline or initial lesion area, lesion growth rate, foveal involvement, and focality. DL experiments were performed using the 2-dimensional U-Net, whole-lesion and multiclass models were developed. Main Outcome MeasuresThe performance of the models was evaluated by calculating the Dice score, coefficient of determination (R2), and the squared Pearson correlation coefficient (r2) between the true and derived GA lesion 1-year ROG. ResultsThe model using baseline and 6-month FAF images to predict GA lesion enlargement at 1.5 years had the best performance for the derived 1-year ROG. Mean Dice scores were 0.73, 0.68, and 0.70, respectively, in the training, validation, and test sets. The R2 (0.77, 0.53, and 0.79) and r2 (0.83, 0.61, and 0.79) showed similar trends across the 3 sets. ConclusionsThese findings show the potential of using baseline and/or 6-month visit FAF images to predict 1-year GA ROG using a DL approach. This work could potentially help support decision making in clinical trials and more informed treatment decisions in clinical practice.

Construction, evidence of validity and reliability of a Teacher Socioemotional Competencies Scale - ECSED

Social-emotional competencies are complex actions that include skills, knowledge and attitudes to maintain effective and satisfactory social interactions. As well as, managing one's own and other people's emotions in different environments. They are vital for healthy coexistence in higher education. The purpose of this research was to establish the evidence of validity and reliability for the scale of socioemotional competencies of university teachers. Scale constructed under the pentagonal model approach of Bizquerra and Pérez (2007). The study is cross-sectional and instrumental. A non-probabilistic convenience sample of 237 teachers from public and private universities in Lima (Peru), from different professional careers, aged between 24 and 70 years, male and female, was used. Satisfactory evidence of content-based validity was found for the criteria of clarity, pertinence, and relevance for each of the items using Aiken's V technique. For validity based on the internal structure of the construct, a Penta dimensional model and a bi-factor model were contrasted by confirmatory factor analysis, with better fit values for the second model (CFI and TLI > 0.95, RMSEA and SRMR < 0.07, = ω h = 0.90, ECV and PUC > 0.95), and favorable evidence of convergent and discriminant validity was also found. For both the overall score and the scores for each factor, there were coefficients denoting high reliability (ordinal α and ω > 0.90).

Characterizing the role of unpredictability within different dimensions of early life adversity.

Dimensional models of early life adversity highlight the distinct roles of deprivation and threat in shaping neurocognitive development and mental health. However, relatively little is known about the role of unpredictability within each dimension. We estimated both the average levels of, and the temporal unpredictability of deprivation and threat exposure during adolescence in a high-risk, longitudinal sample of 1354 youth (Pathways to Desistance study). We then related these estimates to later life psychological distress, and Antisocial and Borderline personality traits, and tested whether any effects are mediated by future orientation. High average levels of both deprivation and threat exposure were found to be associated with worse mental health on all three outcomes, but only the effects on Antisocial and Borderline personality traits were mediated by decreased future orientation, a pattern consistent with evolutionary models of psychopathology. Unpredictability in deprivation exposure proved to be associated with increased psychological distress and a higher number of Borderline traits, but with increased future orientation. There was some evidence of unpredictability in threat exposure buffering against the detrimental developmental effects of average threat levels. Our results suggest that the effects of unpredictability are distinct within different dimensions of early life adversity.

Transgenerational impact of maternal adverse childhood experiences on children's mental health among families experiencing homelessness

BackgroundAdverse childhood experiences (ACEs) threaten the livelihood of individuals and their families. This may be particularly detrimental for mothers facing homelessness and their children's behavioral adjustment. ObjectiveThis study compared two models to examine the impact of maternal ACEs on children's internalizing and externalizing symptoms. Rather than using the cumulative approach to conceptualize ACEs, we analyzed a model that assessed the effect of each maternal ACE individually (i.e., specificity model) and another model that categorized them into two subtypes of maltreatment and household dysfunction (i.e., dimensional model). Participants and settingWe evaluated a sample of 164 children and their mothers in a residential vocational training program in Northern California. MethodsACEs for both mother and children and children's behavioral outcomes were collected via questionnaires completed by the mothers with the assistance of trained researchers. Additional demographic data was collected via trained case managers at the residential facility. Two models (i.e., specificity model; dimensional model) were utilized to better understand how to best assess adversity in settings with limited resources. ResultsResults of the specificity ACE model identified maternal childhood physical abuse and parental incarceration as predictors of internalizing problems and parental substance problems as a predictor of externalizing behaviors. The dimensional ACE model indicated that only the broad category of maltreatment predicted internalizing behavioral difficulties. The individual ACE model demonstrated a better fit for the data and highlighted specific events relevant to children's outcomes. ConclusionsThese results suggest that programs focusing on ending generational trauma may benefit from screening participants for individual ACE and tailoring services for mothers and children accordingly.

Revisiting the Lee-Yang singularities in the four-dimensional Ising model: a tribute to the memory of Ralph Kenna

We have studied numerically the Lee-Yang singularities of the four dimensional Ising model at criticality, which is believed to be in the same universality class as the φ44 scalar field theory. We have focused in the numerical characterization of the logarithmic corrections to the scaling of the zeros of the partition function and its cumulative probability distribution, finding a very good agreement with the predictions of the renormalization group computation on the φ44 scalar field theory. To obtain these results, we have extended a previous study [R. Kenna, C. B. Lang, Nucl. Phys., 1993, B393, 461] in which there were computed numerically the first two zeros for L≤24 lattices, to the computation of the first four zeros for L≤64 lattices.